from dataclasses import dataclass
import torch
import torch.nn as nn
import math, time, inspect
from torch.nn import functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as dist
from torch.distributed import destroy_process_group
from typing import Callable
from dataloader import DataLoaderLite
from misc import COLORED_SEPERATOR
from dist import initialize_distributed

@dataclass
class GPTConfig:
    block_size: int = 1024
    vocab_size: int = 50257
    n_layer: int = 12
    n_head: int = 12
    n_embd: int = 768

    def __post_init__(self):
        self.ff_embd = self.n_embd * 4

class MyMLP(nn.Module):
    def __init__(self, config:GPTConfig):
        super(MyMLP, self).__init__()
        self.c_fc = nn.Linear(config.n_embd, config.ff_embd)
        self.gelu = nn.GELU(approximate='tanh')
        self.c_proj = nn.Linear(config.ff_embd, config.n_embd)
        self.c_proj.NANOGPT_SCALE_INIT = 1.0

    def forward(self, x):
        x = self.c_fc(x)
        x = self.gelu(x)
        x = self.c_proj(x)
        return x

class CausalSelfAttention(nn.Module):
    def __init__(self, config:GPTConfig):
        super(CausalSelfAttention, self).__init__()
        assert config.n_embd % config.n_head == 0, "n_embd must be divisible by n_head"

        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
        self.c_proj.NANOGPT_SCALE_INIT = 1.0

        self.n_head = config.n_head
        self.n_embd = config.n_embd
        self.register_buffer(
            'bias',
            torch.tril( # 下三角矩阵
                torch.ones(config.block_size, config.block_size)
            )[torch.newaxis, torch.newaxis] # (1, 1, block_size, block_size)
        )

    def forward(self, x):
        B, T, C = x.shape

        qkv = self.c_attn(x)
        q, k, v = qkv.split(self.n_embd, dim=2)
        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hc)
        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hc)
        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hc)

        # attn:torch.Tensor = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) # (B, nh, T, T)
        # attn = attn.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))
        # attn = F.softmax(attn, dim=-1)
        # y:torch.Tensor = attn @ v # (B, nh, T, hs)

        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)

        y = y.transpose(1, 2).contiguous().view(B, T, C)
        y = self.c_proj(y)
        return y



class Block(nn.Module):
    def __init__(self, config:GPTConfig):
        super(Block, self).__init__()
        self.ln_1 = nn.LayerNorm(config.n_embd)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = nn.LayerNorm(config.n_embd)
        self.mlp = MyMLP(config)

    def forward(self, x):
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x

class GPT(nn.Module):
    def __init__(self, config:GPTConfig):
        super(GPT, self).__init__()
        self.config = config

        self.transformer = nn.ModuleDict(dict(
            wte = nn.Embedding(config.vocab_size, config.n_embd),
            wpe = nn.Embedding(config.block_size, config.n_embd),
            h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
            ln_f = nn.LayerNorm(config.n_embd)
        ))

        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

        # tie weights of the input and output embeddings
        self.transformer.wte.weight = self.lm_head.weight

        self.apply(self._init_weights)

    def _init_weights(self, module:nn.Module):
        if isinstance(module, nn.Linear):
            std = 0.02
            if hasattr(module, "NANOGPT_SCALE_INIT"):
                std *= (2 * self.config.n_layer) ** -0.5
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)

    @classmethod
    def from_pretrained(cls, model_type):
        """Loads pretrained GPT-2 model weights from huggingface"""
        assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
        from transformers import GPT2LMHeadModel
        print("loading weights from pretrained gpt: %s" % model_type)

        # n_layer, n_head and n_embd are determined from model_type
        config_args = {
            'gpt2':         dict(n_layer=12, n_head=12, n_embd=768),  # 124M params
            'gpt2-medium':  dict(n_layer=24, n_head=16, n_embd=1024), # 350M params
            'gpt2-large':   dict(n_layer=36, n_head=20, n_embd=1280), # 774M params
            'gpt2-xl':      dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params
        }[model_type]
        config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints
        config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints
        # create a from-scratch initialized minGPT model
        config = GPTConfig(**config_args)
        model = GPT(config)
        sd = model.state_dict()
        sd_keys = sd.keys()
        sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')] # discard this mask / buffer, not a param

        # init a huggingface/transformers model
        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
        sd_hf = model_hf.state_dict()

        # copy while ensuring all of the parameters are aligned and match in names and shapes
        sd_keys_hf = sd_hf.keys()
        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')] # ignore these, just a buffer
        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')] # same, just the mask (buffer)
        transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
        # basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
        # this means that we have to transpose these weights when we import them
        assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
        for k in sd_keys_hf:
            if any(k.endswith(w) for w in transposed):
                # special treatment for the Conv1D weights we need to transpose
                assert sd_hf[k].shape[::-1] == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k].t())
            else:
                # vanilla copy over the other parameters
                assert sd_hf[k].shape == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k])

        return model

    def configure_optimizers(self, weight_decay:float, learning_rate:float, device : str | torch.device):
        params_dict = {
            pn : p
            for pn, p in self.named_parameters() if p.requires_grad
        }

        decay_params = [
            p
            for n, p in params_dict.items() if p.dim() >= 2
        ]
        nodecay_params = [
            p
            for n, p in params_dict.items() if p.dim() < 2
        ] # 一维参数（bias）或者标量参数不需要被 weight decay
        optim_groups = [
            {'params': decay_params, 'weight_decay': weight_decay},
            {'params': nodecay_params, 'weight_decay': 0.0},
        ]
        num_decay_params = sum(
            p.numel()
            for p in decay_params
        )
        num_nodecay_params = sum(
            p.numel()
            for p in nodecay_params
        )
        print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters") # :, 为数字添加千位分隔符
        print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")

        fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters # 'fused' 出现在 torch.optim.AdamW 的参数列表中，表示支持 fused AdamW
        use_fused = fused_available and 'cuda' in device
        print(f"using fused AdamW: {use_fused}")

        optimizer = torch.optim.AdamW(
            optim_groups,
            lr=learning_rate,
            betas=(0.9, 0.95),
            eps=1e-8,
            fused=use_fused, # only available in torch 2.0+
        )
        return optimizer

    def forward(self, idx, targets=None):
        B, T = idx.size()
        assert T <= self.config.block_size, f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}"

        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
        pos_emb = self.transformer.wpe(pos)
        tok_emb = self.transformer.wte(idx)
        x = tok_emb + pos_emb

        for block in self.transformer.h:
            x = block(x)

        x = self.transformer.ln_f(x)
        logits = self.lm_head(x) # (B, T, vocab_size)
        loss = None
        if targets is not None:
            loss = F.cross_entropy(
                logits.view(-1, logits.size(-1)), # (BT, vocab_size)
                targets.view(-1), # (BT,)
            )
        return logits, loss


def sample_and_generate():
    num_return_sequences = 5
    max_length = 30
    if __name__ == "__main__":
        model = GPT.from_pretrained('gpt2')
        model.eval()
        model.to('cuda')

        import tiktoken
        enc = tiktoken.get_encoding('gpt2')
        tokens = enc.encode("Hello, I am a student from Tsinghua University.")
        tokens = torch.tensor(tokens, dtype=torch.long)
        tokens = tokens.unsqueeze(0).repeat(num_return_sequences, 1) # (B, T)
        x = tokens.to('cuda')

        torch.manual_seed(42)
        torch.cuda.manual_seed(42)
        while x.size(1) < max_length:
            with torch.no_grad():
                logits = model(x)
                logits = logits[:, -1, :] # (B, vocab_size)

                probs = F.softmax(logits, dim=-1) # (B, vocab_size)

                topk_probs, topk_indices = torch.topk(probs, k=50, dim=-1) # (B, k), (B, k)
                ix = torch.multinomial(topk_probs, num_samples=1) # 根据指定的权重随机抽取 num_samples 个样本 (B, 1)

                xcol = torch.gather(topk_indices, dim=-1, index=ix) # (B, 1)

                x = torch.cat((x, xcol), dim=1) # (B, T + 1)

        for i in range(num_return_sequences):
            tokens = x[i, :max_length].tolist()
            decoded = enc.decode(tokens)
            print(">", decoded)

if __name__ == "__main__":

    ddp, ddp_rank, ddp_local_rank, ddp_world_size, master_process, device = initialize_distributed()

    torch.manual_seed(1337)
    torch.cuda.manual_seed(1337)


    total_batch_size = 524288
    B = 32
    T = 1024
    assert total_batch_size % (B * T * ddp_world_size) == 0, "total_batch_size must be divisible by B * T * ddp_world_size"
    grad_accum_steps = total_batch_size // (B * T * ddp_world_size)
    if master_process:
        print(f"total desired batch size: {total_batch_size}")
        print(f"=> calculated gradient accumulation steps: {grad_accum_steps}")


    train_loader = DataLoaderLite(B=B, T=T, process_rank=ddp_rank, num_processes=ddp_world_size)

    torch.set_float32_matmul_precision("high") # 默认是 highest，high 会降低精度，提高效率

    model = GPT(GPTConfig(vocab_size=50304))
    model.to(device)
    model:GPT = torch.compile(model) # 预编译模型
    if ddp:
        model = DDP(model, device_ids=[ddp_local_rank])
    raw_model = model.module if ddp else model


    max_lr = 6e-4
    min_lr = max_lr * 0.1
    warmup_steps = 10
    max_steps = 50
    def get_lr(it):
        if it < warmup_steps:
            return max_lr * (it + 1) / warmup_steps
        if it > max_steps:
            return min_lr

        decay_ratio = (it - warmup_steps) / (max_steps - warmup_steps)
        assert 0 <= decay_ratio <= 1
        coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
        return min_lr + coeff * (max_lr - min_lr)


    optimizer = raw_model.configure_optimizers(
        weight_decay=0.1,
        learning_rate=6e-4,
        device=device
    )
    for step in range(max_steps):
        t0 = time.time()
        optimizer.zero_grad()
        loss_accum = 0.0
        for micro_step in range(grad_accum_steps): # 梯度累积
            x, y = train_loader.next_batch()
            x, y = x.to(device), y.to(device)
            with torch.autocast(device_type='cuda', dtype=torch.float16): # 自动混合精度训练，只能用于前向传播和loss的计算
                logits, loss = model(x, y)
                # import code; code.interact(local=locals()) # 从当前作用域进入交互式调试
            loss = loss / grad_accum_steps # 梯度累积时注意 loss 的均值问题！！！
            loss_accum += loss.detach()
            if ddp:
                model.require_backward_grad_sync = (micro_step == grad_accum_steps - 1)
            loss.backward() # backward  不能放在 torch.autocast 里面
        if ddp:
            dist.all_reduce(loss_accum, op=dist.ReduceOp.AVG)
        norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)

        lr = get_lr(step)
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr
        optimizer.step()

        torch.cuda.synchronize() # 如果需要对训练作计时，这一步是必不可少的
        t1 = time.time()
        dt = (t1 - t0) * 1000

        tokens_processed = train_loader.B * train_loader.T * grad_accum_steps * ddp_world_size
        tokens_per_sec = tokens_processed / (t1 - t0) # 每秒处理的 token 数
        if master_process:
            print(f"step {step} {COLORED_SEPERATOR} loss: {loss_accum.item():.6f} {COLORED_SEPERATOR} lr: {lr:.4e} {COLORED_SEPERATOR} norm: {norm:.4f} {COLORED_SEPERATOR} dt: {dt:.2f} ms {COLORED_SEPERATOR} tokens/sec: {tokens_per_sec:.2f}")

    if ddp:
        destroy_process_group()